Supplementary material for the paper "TRADITIONAL AND DIGITAL TYPOLOGIES COMPARED: THE EXAMPLE OF ITALIAN PROTOHISTORY"¶

In [ ]:
#dataset
Out[ ]:
ids typology context type Class grave pag fig/tav n Taxonomic_index weighted_taxonomic_index
908 PAD_0002 Cardarelli 2014 Copezzato A5 Vasi a profilo continuo NaN 472 271 37985b 5 1
909 PAD_0003 Cardarelli 2014 Copezzato A5 Vasi a profilo continuo NaN 472 271 41380b 5 1
910 PAD_0004 Cardarelli 2014 Copezzato A5 Vasi a profilo continuo NaN 472 271 56163 5 1
911 PAD_0005 Cardarelli 2014 Copezzato A5 Vasi a profilo continuo NaN 472 271 56165 5 1
914 PAD_0008 Cardarelli 2014 Capriano del Colle A5 Vasi a profilo continuo A 472 271 T.A 5 1
... ... ... ... ... ... ... ... ... ... ... ...
634 TRRGLL_0179 Pacciarelli 1993 Torre Galli K2 Pissidi 110 303 77 4 66 54
651 TRRGLL_0199 Pacciarelli 1993 Torre Galli K2 Pissidi 119 310 84A 2 66 54
757 TRRGLL_0319 Pacciarelli 1993 Torre Galli K2 Pissidi 183 349 123B 1 66 54
678 TRRGLL_0228 Pacciarelli 1993 Torre Galli K4 Pissidi 133 320 94 4 68 55
814 TRRGLL_0378 Pacciarelli 1993 Torre Galli K4 Pissidi 217 373 147A 4 68 55

1149 rows × 11 columns

Cardarelli 2014¶

In [ ]:
for digital_type in pipeline_pots.AI_type.values:
    selected_digit_type = no_noise.loc[no_noise.AI_type == digital_type]
    plot_digital_types(data = selected_pots[selected_digit_type.index], info_selected = selected_digit_type, show_id=True, pot_title="ids", sub_title="type", )
Digital type: 73

Digital type: 68

Digital type: 11

Digital type: 26

Digital type: 63

Digital type: 34

Digital type: 84

Digital type: 62

Digital type: 35

Digital type: 29

Digital type: 12

Digital type: 93

Digital type: 75

Digital type: 86

Digital type: 5

Digital type: 87

Digital type: 74

Digital type: 92

Digital type: 10

Digital type: 99

Digital type: 82

Digital type: 70

Digital type: 52

Digital type: 55

Digital type: 57

Digital type: 61

Digital type: 49

Digital type: 27

Digital type: 56

Digital type: 37

Digital type: 36

Digital type: 66

Digital type: 80

Digital type: 83

Digital type: 69

Digital type: 38

Digital type: 102

Digital type: 25

Digital type: 72

Digital type: 103

Digital type: 101

Digital type: 51

Digital type: 100

Digital type: 91

Digital type: 98

Digital type: 88

Digital type: 81

Digital type: 33

Digital type: 77

Digital type: 94

Digital type: 78

Digital type: 40

Digital type: 43

Digital type: 44

Digital type: 41

Digital type: 23

Digital type: 67

Digital type: 59

Digital type: 4

Digital type: 90

Digital type: 96

Digital type: 95

Digital type: 76

Digital type: 97

Digital type: 3

Digital type: 60

Digital type: 71

Digital type: 32

Digital type: 20

Digital type: 39

Digital type: 28

Digital type: 0

Digital type: 14

Digital type: 89

Digital type: 58

Digital type: 85

Digital type: 21

Digital type: 79

Digital type: 22

Digital type: 42

Digital type: 50

Digital type: 64

Digital type: 6

Digital type: 1

Digital type: 65

Digital type: 8

Digital type: 13

Digital type: 7

Digital type: 53

Digital type: 17

Digital type: 9

Digital type: 45

Digital type: 31

Digital type: 18

Digital type: 24

Digital type: 47

Digital type: 48

Digital type: 15

Digital type: 54

Digital type: 19

Digital type: 16

Digital type: 30

Digital type: 46

Digital type: 2

In [ ]:
pipeline_pots.mean()
C:\Users\larth\AppData\Local\Temp/ipykernel_6940/2181277370.py:1: FutureWarning:

Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.

Out[ ]:
AI_type                   51.500000
Mean_pot_value             0.679524
N_pots                     4.769231
Tassonomic_variability    14.488668
dtype: float64
In [ ]:
pipeline_pots
Out[ ]:
AI_type Mean_pot_value N_pots Tassonomic_variability correspondence
0 73 0.485340 10 50.140203 NaN
1 68 0.608384 5 24.489998 NaN
2 11 0.594954 6 2.426703 ['A6']
3 26 0.486480 8 41.036379 ['I16']
4 63 0.789930 3 2.160247 NaN
5 34 0.521257 5 25.487252 NaN
6 84 0.568523 8 34.115383 NaN
7 62 0.693291 3 35.863940 NaN
8 35 0.622356 5 28.322429 NaN
9 29 0.655536 6 21.090414 ['A11']
10 12 0.606977 6 24.178158 NaN
11 93 0.628005 6 4.509250 ['A16']
12 75 0.726473 4 19.723083 NaN
13 86 0.810663 3 24.097026 NaN
14 5 0.526529 5 16.473008 ['A13']
15 87 0.607810 7 24.448196 NaN
16 74 0.671594 4 25.114737 ['A13']
17 92 0.742343 3 1.247219 NaN
18 10 0.518019 7 16.461458 NaN
19 99 0.590600 7 17.307542 NaN
20 82 0.666185 6 21.587033 NaN
21 70 0.634974 6 19.379256 ['E7']
22 52 0.720370 4 32.264532 NaN
23 55 0.686006 3 34.422215 NaN
24 57 0.665197 8 2.000000 ['A24']
25 61 0.618022 6 23.893281 NaN
26 49 0.511090 7 33.289699 NaN
27 27 0.903798 3 24.984440 ['A21']
28 56 0.800793 3 1.414214 ['A24']
29 37 0.713657 5 22.471315 ['A24']
30 36 0.505102 4 20.825165 NaN
31 66 0.600979 6 25.486925 NaN
32 80 0.650199 6 20.941718 NaN
33 83 0.718339 5 18.301912 NaN
34 69 0.692422 4 19.163768 NaN
35 38 0.700282 3 20.832667 NaN
36 102 0.748013 3 22.627417 ['A26']
37 25 0.611917 6 18.743888 NaN
38 72 0.735388 4 22.387217 ['E15']
39 103 0.738375 5 18.015549 NaN
40 101 0.728180 5 8.231646 NaN
41 51 0.566719 9 25.407785 NaN
42 100 0.683503 4 5.402546 ['C8']
43 91 0.674654 4 8.042854 NaN
44 98 0.604506 5 21.872357 NaN
45 88 0.732746 4 17.014700 ['C9']
46 81 0.806357 3 4.242641 ['C6']
47 33 0.550698 9 18.156385 NaN
48 77 0.677066 3 22.171052 NaN
49 94 0.586193 4 24.233242 NaN
50 78 0.567453 6 25.230603 ['C1']
51 40 0.641938 5 18.183509 NaN
52 43 0.573154 7 23.100490 NaN
53 44 0.630409 6 19.567547 NaN
54 41 0.698663 4 20.000000 ['C4', 'E17']
55 23 0.633834 3 22.231109 NaN
56 67 0.614326 4 27.000000 ['C5', 'F11']
57 59 0.597576 7 19.353848 NaN
58 4 0.808023 3 26.398653 ['C6']
59 90 0.621471 4 3.112475 NaN
60 96 0.642158 3 18.624953 NaN
61 95 0.663726 4 7.088723 ['C7']
62 76 0.753362 4 16.170962 ['E17']
63 97 0.770698 3 15.839472 NaN
64 3 0.752593 4 20.820663 NaN
65 60 0.748334 4 9.246621 ['E3']
66 71 0.811515 3 11.313708 ['E7']
67 32 0.600710 3 2.494438 NaN
68 20 0.453305 7 1.277753 NaN
69 39 0.740107 4 4.690416 ['E12']
70 28 0.787392 3 3.771236 ['E3']
71 0 0.817432 3 14.839886 NaN
72 14 0.639674 6 23.900256 NaN
73 89 0.682601 4 0.000000 ['E15']
74 58 0.797921 4 9.836158 ['G2']
75 85 0.722972 3 1.414214 ['F8']
76 21 0.672399 4 1.500000 ['F11']
77 79 0.558541 5 0.748331 NaN
78 22 0.735293 3 11.145502 NaN
79 42 0.604754 5 5.621388 NaN
80 50 0.508626 4 7.632169 NaN
81 64 0.583859 6 6.633250 ['L3']
82 6 0.531010 9 7.166451 NaN
83 1 0.734444 4 6.796139 ['N3']
84 65 0.742748 4 2.277608 ['I5']
85 8 0.741607 4 12.028612 NaN
86 13 0.787773 3 12.675436 NaN
87 7 0.684782 5 5.946427 NaN
88 53 0.811981 3 9.933110 NaN
89 17 0.700820 6 4.374802 NaN
90 9 0.812315 3 4.242641 ['O9']
91 45 0.532238 11 4.028822 NaN
92 31 0.704690 9 2.997942 NaN
93 18 0.702741 4 1.299038 ['O6']
94 24 0.793171 3 2.357023 ['O4']
95 47 0.849786 3 2.357023 ['O4']
96 48 0.777858 4 2.000000 ['O5', 'O9']
97 15 0.798444 4 2.165064 ['O6']
98 54 0.807193 4 1.500000 ['O6', 'O9']
99 19 0.808974 3 2.357023 ['O6']
100 16 0.849746 3 2.449490 NaN
101 30 0.849078 3 0.000000 ['O6']
102 46 0.696284 5 1.166190 ['O12']
103 2 0.831171 3 1.414214 ['O12']

Bianco Peroni et al. 2010¶

In [ ]:
for digital_type in pipeline_pots.AI_type.values:
    selected_digit_type = no_noise.loc[no_noise.AI_type == digital_type]
    plot_digital_types(data = selected_pots[selected_digit_type.index], info_selected = selected_digit_type, show_id=True, pot_title="ids", sub_title="type", )
Digital type: 0

Digital type: 6

Digital type: 7

Digital type: 2

Digital type: 3

Digital type: 8

Digital type: 19

Digital type: 20

Digital type: 14

Digital type: 12

Digital type: 1

Digital type: 18

Digital type: 13

Digital type: 31

Digital type: 11

Digital type: 30

Digital type: 16

Digital type: 5

Digital type: 17

Digital type: 24

Digital type: 9

Digital type: 4

Digital type: 22

Digital type: 10

Digital type: 25

Digital type: 33

Digital type: 27

Digital type: 26

Digital type: 28

Digital type: 23

Digital type: 29

Digital type: 32

Digital type: 15

Digital type: 35

Digital type: 34

Digital type: 36

Digital type: 21

In [ ]:
pipeline_pots
Out[ ]:
AI_type Mean_pot_value N_pots Tassonomic_variability Correspondence
0 0 0.634835 5 6.740920 NaN
1 6 0.658898 4 7.449832 NaN
2 7 0.746334 3 3.299832 ['8']
3 2 0.568266 8 4.728570 NaN
4 3 0.632567 4 0.866025 ['7']
5 8 0.499502 7 3.251373 NaN
6 19 0.732170 4 1.785357 ['13']
7 20 0.589643 4 1.089725 ['11']
8 14 0.455669 5 23.215512 NaN
9 12 0.595627 8 5.612486 NaN
10 1 0.665467 4 7.361216 ['39']
11 18 0.624761 5 1.743560 NaN
12 13 0.663299 5 3.059412 NaN
13 31 0.775303 5 2.939388 ['37']
14 11 0.676464 3 3.299832 NaN
15 30 0.769901 3 2.494438 NaN
16 16 0.803873 3 2.357023 ['37']
17 5 0.612095 7 2.050386 NaN
18 17 0.659420 3 0.942809 ['40']
19 24 0.652675 3 10.842304 ['44']
20 9 0.563237 5 7.949843 NaN
21 4 0.503236 7 5.678459 NaN
22 22 0.608299 4 7.500000 NaN
23 10 0.591072 6 6.693695 NaN
24 25 0.493288 7 7.342913 NaN
25 33 0.648729 4 7.154544 NaN
26 27 0.623472 5 7.445804 NaN
27 26 0.582084 5 9.744742 NaN
28 28 0.572744 3 2.357023 ['57']
29 23 0.586778 5 4.841487 NaN
30 29 0.565970 4 2.947457 NaN
31 32 0.509599 6 4.856267 NaN
32 15 0.478703 6 4.932883 ['71']
33 35 0.703754 3 1.885618 ['66']
34 34 0.594473 4 1.224745 ['63']
35 36 0.670121 3 0.471405 ['63']
36 21 0.587405 4 1.500000 ['64']

Pacciarelli 1993¶

In [ ]:
for digital_type in pipeline_pots.AI_type.values:
    selected_digit_type = no_noise.loc[no_noise.AI_type == digital_type]
    plot_digital_types(data = selected_pots[selected_digit_type.index], info_selected = selected_digit_type, show_id=True, pot_title="ids", sub_title="type", )
Digital type: 22

Digital type: 76

Digital type: 51

Digital type: 46

Digital type: 36

Digital type: 34

Digital type: 23

Digital type: 44

Digital type: 50

Digital type: 70

Digital type: 40

Digital type: 20

Digital type: 43

Digital type: 59

Digital type: 58

Digital type: 35

Digital type: 47

Digital type: 56

Digital type: 67

Digital type: 21

Digital type: 69

Digital type: 33

Digital type: 68

Digital type: 75

Digital type: 45

Digital type: 13

Digital type: 60

Digital type: 57

Digital type: 61

Digital type: 42

Digital type: 41

Digital type: 8

Digital type: 66

Digital type: 0

Digital type: 3

Digital type: 62

Digital type: 27

Digital type: 39

Digital type: 24

Digital type: 65

Digital type: 77

Digital type: 74

Digital type: 37

Digital type: 14

Digital type: 52

Digital type: 64

Digital type: 78

Digital type: 80

Digital type: 71

Digital type: 79

Digital type: 72

Digital type: 73

Digital type: 26

Digital type: 38

Digital type: 4

Digital type: 63

Digital type: 53

Digital type: 25

Digital type: 6

Digital type: 49

Digital type: 15

Digital type: 48

Digital type: 7

Digital type: 5

Digital type: 54

Digital type: 17

Digital type: 16

Digital type: 2

Digital type: 1

Digital type: 28

Digital type: 29

Digital type: 19

Digital type: 18

Digital type: 30

Digital type: 31

Digital type: 12

Digital type: 9

Digital type: 32

Digital type: 10

Digital type: 11

Digital type: 55

In [ ]:
pipeline_pots
Out[ ]:
AI_type Mean_pot_value N_pots Tassonomic_variability Correspondence
0 22 0.618766 10 5.491812 NaN
1 76 0.635474 5 5.713143 ['Ab7']
2 51 0.671436 9 5.395471 NaN
3 46 0.633913 8 4.897385 NaN
4 36 0.632914 6 4.099458 NaN
5 34 0.588158 5 5.919459 NaN
6 23 0.711511 5 9.046546 NaN
7 44 0.649752 3 5.887841 NaN
8 50 0.652886 7 5.499536 NaN
9 70 0.780865 3 7.118052 NaN
10 40 0.732023 3 2.160247 NaN
11 20 0.690300 7 5.576920 NaN
12 43 0.728763 4 5.448624 ['Ab3']
13 59 0.719413 7 5.154748 NaN
14 58 0.735544 4 7.648529 NaN
15 35 0.770060 3 6.342099 NaN
16 47 0.631722 7 6.249898 NaN
17 56 0.766230 4 3.561952 NaN
18 67 0.740480 4 6.576473 NaN
19 21 0.747476 4 4.716991 ['Aa3']
20 69 0.737245 4 5.196152 ['Aa3']
21 33 0.725741 4 5.356071 ['Aa4']
22 68 0.830434 3 4.966555 NaN
23 75 0.560716 10 6.053098 NaN
24 45 0.675877 4 5.309190 NaN
25 13 0.668273 6 4.524624 NaN
26 60 0.574324 8 5.808130 NaN
27 57 0.748515 3 5.887841 NaN
28 61 0.663397 7 5.091008 NaN
29 42 0.618484 5 5.741080 NaN
30 41 0.807720 3 2.160247 NaN
31 8 0.723270 4 4.493050 ['Ab3']
32 66 0.670312 3 0.942809 ['Ab8']
33 0 0.690373 3 2.449490 NaN
34 3 0.665579 7 5.070926 NaN
35 62 0.473840 9 6.699917 NaN
36 27 0.469722 6 7.174414 NaN
37 39 0.594476 7 2.871393 NaN
38 24 0.629725 5 3.187475 NaN
39 65 0.627572 5 0.632456 ['C2']
40 77 0.625282 4 1.500000 ['C2']
41 74 0.530002 6 1.572330 NaN
42 37 0.633045 4 5.787918 NaN
43 14 0.589972 8 2.384848 ['C2']
44 52 0.659325 6 3.337497 NaN
45 64 0.659005 4 0.000000 ['C2']
46 78 0.621732 3 8.013877 ['C2']
47 80 0.672914 4 1.479020 NaN
48 71 0.687569 3 0.000000 ['C2']
49 79 0.731336 3 0.000000 ['C2']
50 72 0.681448 3 3.741657 NaN
51 73 0.689286 4 1.500000 ['C2', 'C5']
52 26 0.616178 4 6.869316 NaN
53 38 0.497654 7 14.202615 NaN
54 4 0.708484 3 2.160247 NaN
55 63 0.675607 3 6.683313 NaN
56 53 0.626034 4 1.920286 ['C6']
57 25 0.802283 3 2.494438 NaN
58 6 0.449445 6 9.370462 NaN
59 49 0.740533 3 4.714045 ['D4']
60 15 0.556257 4 2.449490 ['E2']
61 48 0.675562 3 0.471405 ['D4']
62 7 0.494803 6 7.542472 ['G2']
63 5 0.577295 4 0.000000 ['E2']
64 54 0.471586 6 7.909207 ['J1']
65 17 0.474334 12 2.763854 ['H5']
66 16 0.556280 5 3.720215 NaN
67 2 0.468532 9 4.771313 NaN
68 1 0.652996 3 4.784233 NaN
69 28 0.541868 5 9.987993 NaN
70 29 0.561741 8 1.854050 NaN
71 19 0.504932 6 2.808717 ['H6']
72 18 0.647437 3 3.559026 NaN
73 30 0.610656 5 1.833030 NaN
74 31 0.631988 3 3.559026 NaN
75 12 0.579866 4 2.947457 ['H3']
76 9 0.521097 6 1.674979 ['H7']
77 32 0.700630 3 1.699673 NaN
78 10 0.452418 6 1.154701 ['H7']
79 11 0.644878 3 0.471405 ['H5']
80 55 0.729357 3 0.471405 ['K2']